class_docstr: Simple full-connected layer neural network composed of a sequence of linear layers with PReLU activation and
  dropout.  The network accepts input with `in_channels` channels, has output with `out_channels` channels, and
  hidden layer output channels given in `hidden_channels`. If `bias` is True then linear units have a bias term.
  
  Args:
      in_channels: number of input channels.
      out_channels: number of output channels.
      hidden_channels: number of output channels for each hidden layer.
      dropout: dropout ratio. Defaults to no dropout.
      act: activation type and arguments. Defaults to PReLU.
      bias: whether to have a bias term in linear units. Defaults to True.
      adn_ordering: order of operations in :py:class:`monai.networks.blocks.ADN`.
  
  Examples::
  
      # accepts 4 values and infers 3 values as output, has 3 hidden layers with 10, 20, 10 values as output
      net = FullyConnectedNet(4, 3, [10, 20, 10], dropout=0.2)
class_docstr_tok: ["Simple","full-connected","layer","neural","network","composed","of","a","sequence","of","linear","layers","with","PReLU","activation","and","dropout",".","The","network","accepts","input","with","`","in_channels","`","channels",",","has","output","with","`","out_channels","`","channels",",","and","hidden","layer","output","channels","given","in","`","hidden_channels","`",".","If","`","bias","`","is","True","then","linear","units","have","a","bias","term",".","Args",":","in_channels",":","number","of","input","channels",".","out_channels",":","number","of","output","channels",".","hidden_channels",":","number","of","output","channels","for","each","hidden","layer",".","dropout",":","dropout","ratio",".","Defaults","to","no","dropout",".","act",":","activation","type","and","arguments",".","Defaults","to","PReLU",".","bias",":","whether","to","have","a","bias","term","in","linear","units",".","Defaults","to","True",".","adn_ordering",":","order","of","operations","in",":","py",":","class",":","`","monai.networks.blocks.ADN","`",".","Examples",":",":","#","accepts","4","values","and","infers","3","values","as","output",",","has","3","hidden","layers","with","10",",","20",",","10","values","as","output","net","=","FullyConnectedNet","(","4",",","3",",","[","10",",","20",",","10","]",",","dropout=0.2",")"]
code: def _get_layer(
      self, in_channels: int, out_channels: int, bias: bool
  ) -> nn.Sequential:
      seq = nn.Sequential(
          nn.Linear(in_channels, out_channels, bias),
          _get_adn_layer(self.act, self.dropout, self.adn_ordering),
      )
      return seq
  
file_path: MONAI/monai/networks/nets/fullyconnectednet.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks import ADN
  from monai.networks.layers.factories import Act
label_desc: Use image node_id 2 for calling the FullyConnectedNet obj's underlying member method code with example usage: obj._get_layer(in_channels, out_channels, bias) and returns: seq